Teaching

My teaching philosophy is guided by three principles: Purpose, Flexibility, and Fairness.

• Purpose. With every policy, activity, and assignment, I try to remain mindful about what its purpose is. This purpose can be anything –- from learning a crucial concept to just having fun because the semester is long. In my experience, students appreciate this clarity and transparency, knowing not just the "what", but also the "how" and "why".

• Flexibility. There's tremendous heterogeneity in interests and needs between students, and it's my responsibility to deliver the best course I can to each student. I see my role as both a leader and a guide in the classroom -- leading students through the defined course, while guiding them to dig deeper.

• Fairness. I’m lucky to be the son of a university’s ombudsperson. From my mother, I’ve learned the importance of considering fairness first and foremost in my policies and decisions.

My undergraduate education at The Ivey Business School centered around case discussions of real business problems. As a result, I place tremendous value on having students practice decision-making. Universities offer a safe place to practice making and defending decisions, an opportunity I take advantage of in my classes.

As a graduate student, I've developed and graded homework assignments in R for a customer analytics course, and on my own time have learned machine learning and web scraping. I would love to bring those experiences into the classroom to teach customer analytics-types of courses.

I am incredibly lucky to have the right kind of mind to understand data and programming. I am also lucky to be passionate about continually improving my data analytic abilities in R and Python. As a result, I have always enjoyed helping classmates and students to learn about these things.

I love to teach courses in customer analytics to both undergrad and graduate students. As data becomes even more prevalent and powerful in business, it is becoming less possible for employees -- in any role -- to not understand data science to at least a basic level.

I would love to design a course that fills that need -- data science for people who are scared of data science -- as well as more common data analytics classes.

As an Instructor:

• MKTG 2700-300E: Digital Marketing Tools

  • Spring 2021: 1 Section — Average Evaluation of 4.96/5

  • Spring 2022: 1 Section — Average Evaluation of 4.81/5

As a Teaching Assistant:

• MSBX 5310: Customer Analytics (2019, 2020 — 3 sections total)

• MSBX 5410: Fundamentals of Data Analytics (2022 — 3 sections)

• MKTG 3700: Digital Marketing (2020 — 2 sections)

• MBAX 6350: Digital Marketing (2020)